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[2501.17860] Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
Summary
This article presents a novel approach to training medical large language models (LLMs) through dialogue-based fine-tuning, improving their clinical reasoning capabilities in real-world scenarios.
Why It Matters
The study addresses critical shortcomings in current medical AI systems, which often fail to replicate real-world clinical reasoning. By introducing a dialogue-based training method, it enhances the models' ability to handle complex, noisy environments, making them more applicable in clinical settings. This advancement could lead to more reliable AI tools in healthcare, ultimately improving patient outcomes.
Key Takeaways
- Dialogue-based fine-tuning significantly improves medical LLM performance.
- The proposed benchmark simulates real-world diagnostic scenarios.
- Models showed a 9.64% improvement in multi-round reasoning tasks.
- Integrating noise and distractions aligns training with USMLE standards.
- Dialogue tuning is a promising method for enhancing clinical AI systems.
Computer Science > Computation and Language arXiv:2501.17860 (cs) [Submitted on 29 Jan 2025 (v1), last revised 21 Feb 2026 (this version, v2)] Title:Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations Authors:Zijie Liu, Xinyu Zhao, Jie Peng, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen View a PDF of the paper titled Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations, by Zijie Liu and 7 other authors View PDF HTML (experimental) Abstract:Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks. These tuning methods and benchmarks overlook critical aspects like evidence-based reasoning and handling distracting information. To bridge this gap, we introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards. Moreover, we explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes. Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64\%$ in multi-round reasoning scenarios and $6.18\%$ in accuracy in a noisy environment. Our findings highlight dialogue tuning as a promising approach for advancing clinically aligned and robust medical AI systems. Comments: Subjects...
